Triple
T5601598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dike |
E147131
|
entity |
| Predicate | typeOfJustice |
P40179
|
FINISHED |
| Object | moral justice |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: moral justice | Statement: [Dike, typeOfJustice, moral justice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfJustice Context triple: [Dike, typeOfJustice, moral justice]
-
A.
typeOfJusticeMechanism
chosen
Indicates the kind or category of justice mechanism that characterizes how justice is pursued or administered in a given context.
-
B.
typeOfJurisdiction
Indicates the specific kind or category of legal authority or control that one jurisdiction holds in relation to a given legal or administrative context.
-
C.
typeOfLaw
Indicates that one entity is a specific category or kind of law to which the other entity pertains.
-
D.
hasTypeOfCourt
Indicates that an entity is associated with or classified by a specific type or category of court.
-
E.
courtStyle
Indicates the manner or stylistic approach in which a court conducts its proceedings or presents its decisions.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c009043d648190a7af89698ccf1e3e |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020dbd6dc8190ba011876c205754e |
completed | March 22, 2026, 5:03 p.m. |
| PD | Predicate disambiguation | batch_69c01b1890ec8190b9e6fa488792e4d4 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:39 p.m.